A Comparative Study of Machine-Learning Indoor Localization Using FM and DVB-T Signals in Real Testbed Environments

Yen Kai Cheng, Ronald Y. Chang, Ling Jyh Chen

研究成果: 書貢獻/報告類型會議論文篇章

3 引文 斯高帕斯(Scopus)

摘要

Wireless indoor localization is a key technology for the future Internet of things (IoT) paradigm. In this paper, we perform an experimental comparative study of machine learning-based localization schemes, such as k-nearest neighbor (k-NN) and variants of support vector machine (SVM), based on the received signal strength (RSS) measurements of the ambient frequency modulation (FM) and digital video broadcasting- terrestrial (DVB-T) signals in three real testbed environments. The consideration of readily available, ambient radio signals frees the need for dedicated radio transmitters. Noise-reduction techniques such as feature selection and ensemble learning are proposed in conjunction with SVM. Our results examine the performance comparisons between SVM and k-NN, as well as the performance comparisons of SVM-based methods incorporating different noise-reduction schemes, with noisy RSS data. Insights into the performance of learning- based localization schemes working with real database collected from real environments are provided.

原文英語
主出版物標題2017 IEEE 85th Vehicular Technology Conference, VTC Spring 2017 - Proceedings
發行者Institute of Electrical and Electronics Engineers Inc.
ISBN(電子)9781509059324
DOIs
出版狀態已發佈 - 2017 十一月 14
對外發佈
事件85th IEEE Vehicular Technology Conference, VTC Spring 2017 - Sydney, 澳大利亚
持續時間: 2017 六月 42017 六月 7

出版系列

名字IEEE Vehicular Technology Conference
2017-June
ISSN(列印)1550-2252

會議

會議85th IEEE Vehicular Technology Conference, VTC Spring 2017
國家/地區澳大利亚
城市Sydney
期間2017/06/042017/06/07

ASJC Scopus subject areas

  • 電腦科學應用
  • 電氣與電子工程
  • 應用數學

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